结合判别分析和分布差异约束的领域适应方法OACSTPCD

To solve the problem of category structure loss and local feature loss caused by feature transformation based on global distribution adaptation in the process of domain adaptation,a domain adaptation method combined with discrim-inant analysis and distribution discrepancy constraints is proposed.Firstly,the mean distance metric of the domain data distribution is constructed for domain distribution adaptation;secondly,the class divergence metric is constructed to main-tain the class discriminant structure;meanwhile,based on the sample local distribution information,multiple discrepancy weights are designed to constrain the domain distribution discrepancy measure and the class discrimination measure respectively,which realizes the joint optimization of discriminant preservation and locality preservation;finally,based on the feature transformation optimized by the metrics aforementioned,the source domain data and target domain data are projected into the subspace for classification task.During the process of domain adaptation,the proposed method can not only reduce the distribution difference between domains,but also take into account the preservation of category discrimi-nation and local characteristics of data,thus effectively improve the performance of reusage of out-of-domain data.The experimental results on 28 cross-domain classification tasks show that the proposed method significantly outperforms the other related methods in terms of the evaluation metric.